Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models.
Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models.
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DOI：
10.1002/sim.3900
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发表时间：
2010-07-20
影响因子：
2
通讯作者：
Miranda, Marie L.
中科院分区：
文献类型：
--
作者：
Schwartz, Scott L.;Gelfand, Alan E.;Miranda, Marie L.
出生体重和胎龄密切相关，是健康妊娠的重要指标。通常对出生体重的建模取决于胎龄。然而，联合建模直接解决了胎龄和出生体重之间的关系，并提供了更大的灵活性和解释，以及避免将胎龄作为中间变量的策略。以前的建议利用有限混合的双变量回归模型，将公认的危险因素纳入分析(例如，婴儿的性别和出生顺序、母亲的年龄、种族和烟草使用)，同时检查联合出生体重和胎龄分布的非高斯形状。我们以这种方法为基础，展示了联合建模的推论(预测)好处(例如，调查不适合年龄的结果，如小于胎龄)，并因此再次强调捕捉非高斯分布形状的重要性。此外，我们还通过允许间隔删失孕龄的潜在规范对现有模型进行了扩展。我们在贝叶斯框架内工作，这使得推论超越了常规的参数估计和预测，以及准确的不确定性评估。该模型适用于2003年至2006年北卡罗来纳州通过儿童环境健康倡议获得的部分详细出生记录数据(n=336129)，并使用贝叶斯方法和马尔科夫链蒙特卡罗方法进行拟合。
Birthweight and gestational age are closely related and represent important indicators of a healthy pregnancy. Customary modeling for birthweight is conditional on gestational age. However, joint modeling directly addresses the relationship between gestational age and birthweight, and provides increased flexibility and interpretation as well as a strategy to avoid using gestational age as an intermediate variable. Previous proposals have utilized finite mixtures of bivariate regression models to incorporate well-established risk factors into analysis (e.g. sex and birth order of the baby, maternal age, race, and tobacco use) while examining the non-Gaussian shape of the joint birthweight and gestational age distribution. We build on this approach by demonstrating the inferential (prognostic) benefits of joint modeling (e.g. investigation of `age inappropriate' outcomes like small for gestational age) and hence re-emphasize the importance of capturing the non-Gaussian distributional shapes. We additionally extend current models through a latent specification which admits interval-censored gestational age. We work within a Bayesian framework which enables inference beyond customary parameter estimation and prediction as well as exact uncertainty assessment. The model is applied to a portion of the 2003–2006 North Carolina Detailed Birth Record data (n=336129) available through the Children's Environmental Health Initiative and is fitted using the Bayesian methodology and Markov chain Monte Carlo approaches.
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影响因子：
1.7
作者：
Gage, TB
通讯作者：
Gage, TB
DOI：
10.1111/j.1469-1809.1951.tb02469.x
发表时间：
1951-01-01
期刊：
ANNALS OF EUGENICS
影响因子：
--
作者：
KARN, MN;PENROSE, LS
通讯作者：
PENROSE, LS
影响因子：
--
作者：
PANETH, NS
通讯作者：
PANETH, NS
影响因子：
3.7
作者：
Gelfand, AE;Sahu, K
通讯作者：
Sahu, K
影响因子：
6.1
作者：
Delbaere, Ilse;Vansteelandt, Stijn;Temmerman, Marleen
通讯作者：
Temmerman, Marleen